65,868 research outputs found
Neural Networks with Non-Uniform Embedding and Explicit Validation Phase to Assess Granger Causality
A challenging problem when studying a dynamical system is to find the
interdependencies among its individual components. Several algorithms have been
proposed to detect directed dynamical influences between time series. Two of
the most used approaches are a model-free one (transfer entropy) and a
model-based one (Granger causality). Several pitfalls are related to the
presence or absence of assumptions in modeling the relevant features of the
data. We tried to overcome those pitfalls using a neural network approach in
which a model is built without any a priori assumptions. In this sense this
method can be seen as a bridge between model-free and model-based approaches.
The experiments performed will show that the method presented in this work can
detect the correct dynamical information flows occurring in a system of time
series. Additionally we adopt a non-uniform embedding framework according to
which only the past states that actually help the prediction are entered into
the model, improving the prediction and avoiding the risk of overfitting. This
method also leads to a further improvement with respect to traditional Granger
causality approaches when redundant variables (i.e. variables sharing the same
information about the future of the system) are involved. Neural networks are
also able to recognize dynamics in data sets completely different from the ones
used during the training phase
Efficient Parallel Translating Embedding For Knowledge Graphs
Knowledge graph embedding aims to embed entities and relations of knowledge
graphs into low-dimensional vector spaces. Translating embedding methods regard
relations as the translation from head entities to tail entities, which achieve
the state-of-the-art results among knowledge graph embedding methods. However,
a major limitation of these methods is the time consuming training process,
which may take several days or even weeks for large knowledge graphs, and
result in great difficulty in practical applications. In this paper, we propose
an efficient parallel framework for translating embedding methods, called
ParTrans-X, which enables the methods to be paralleled without locks by
utilizing the distinguished structures of knowledge graphs. Experiments on two
datasets with three typical translating embedding methods, i.e., TransE [3],
TransH [17], and a more efficient variant TransE- AdaGrad [10] validate that
ParTrans-X can speed up the training process by more than an order of
magnitude.Comment: WI 2017: 460-46
Cross-dimensional Weighting for Aggregated Deep Convolutional Features
We propose a simple and straightforward way of creating powerful image
representations via cross-dimensional weighting and aggregation of deep
convolutional neural network layer outputs. We first present a generalized
framework that encompasses a broad family of approaches and includes
cross-dimensional pooling and weighting steps. We then propose specific
non-parametric schemes for both spatial- and channel-wise weighting that boost
the effect of highly active spatial responses and at the same time regulate
burstiness effects. We experiment on different public datasets for image search
and show that our approach outperforms the current state-of-the-art for
approaches based on pre-trained networks. We also provide an easy-to-use, open
source implementation that reproduces our results.Comment: Accepted for publications at the 4th Workshop on Web-scale Vision and
Social Media (VSM), ECCV 201
Application of Steganography for Anonymity through the Internet
In this paper, a novel steganographic scheme based on chaotic iterations is
proposed. This research work takes place into the information hiding security
framework. The applications for anonymity and privacy through the Internet are
regarded too. To guarantee such an anonymity, it should be possible to set up a
secret communication channel into a web page, being both secure and robust. To
achieve this goal, we propose an information hiding scheme being stego-secure,
which is the highest level of security in a well defined and studied category
of attacks called "watermark-only attack". This category of attacks is the best
context to study steganography-based anonymity through the Internet. The
steganalysis of our steganographic process is also studied in order to show it
security in a real test framework.Comment: 14 page
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